The shortcomings of pixel-based similarity metrics, such as blur in the generated images, have been studied substantially in the image generation community [1]}, [2]} and have been superseded by deep similarity metrics approximating human visual perception. Here, features are extracted from neural networks pre-trained on image-classification tasks [3]}. Performance can further be improved by fine-tuning the features to human perception [4]}, [2]}, leading to generative models that produce photo-realistic images.
We propose to apply deep similarity metrics within image registration to achieve a similar increase of performance for registration models.
